Obesity is known to be associated with a large number of long-term morbidities, and while in some cases the relationship of obesity and the consequences is clear (for example, excess weight and lower extremity orthopedic problems) in others the mechanism is not as clear. One common system of categorizing overweight in terms of the likelihood of negative consequences involves using the concept of "metabolic syndrome". We hypothesized that the development of a plasma protein profile of overweight adolescents with and without the metabolic syndrome might give a more precise and informative picture of the disease process than the current clinical categorization and permit early targeted intervention. For this paper, we used antibody microarrays to analyze the plasma proteome of a group of 15 overweight female adolescent patients. Upon analysis of the proteome, the overweight patients diverged from the nonoverweight female controls. Furthermore, the overweight patients were divided by the analysis into two population clusters, each with distinctive protein expression patterns. Interestingly, the clusters were characterized by differences in insulin resistance, as measured by HOMA. Categorization according to the presence or absence of the metabolic syndrome did not yield such clusters.
The incidence of cardiovascular diseases is ten-times higher in males than females, although the biological basis for this gender disparity is not known. However, based on the fact that antiplatelet drugs are the mainstay for prevention and therapy, we hypothesized that the signaling proteomes in platelets from normal male donors might be more activated than platelets from normal female donors. We report here that platelets from male donors express significantly higher levels of signaling cascade proteins than platelets from female donors. In silico connectivity analysis shows that the 24 major hubs in platelets from male donors focus on pathways associated with megakaryocytic expansion and platelet activation. By contrast, the 11 major hubs in platelets from female donors were found to be either negative or neutral for platelet-relevant processes. The difference may suggest a biological mechanism for gender discrimination in cardiovascular disease.
Bipolar disorder (BD) is a chronic and often severe psychiatric illness characterized by manic and depressive episodes. Among the most effective treatments, mood stabilizers represent the keystone in acute mania, depression, and maintenance treatment of BD. However, treatment response is a highly heterogeneous trait, thus emphasizing the need for a structured informational framework of phenotypic and genetic predictors. In this paper, we present the current state of pharmacogenomic research on long-term treatment in BD, specifically focusing on mood stabilizers. While the results provided so far support the key role of genetic factors in modulating the response phenotype, strong evidence for genetic predictors is still lacking. In order to facilitate implementation of pharmacogenomics into clinical settings (i.e., the creation of personalized therapy), further research efforts are needed.
Although methotrexate is widely used in clinical practice there remains significant lack of understanding of its mechanisms of action and the factors that contribute to the variability in toxicity and response seen clinically.In addition to differences in drug administration, factors that affect pharmacokinetics and pharmacodynamics such as genetic variation may explain individual differences in drug biotransformation.However, the pediatric population has an additional factor to consider, namely the ontogeny of gene expression which may result in variation throughout growth and development.We review the current understanding of methotrexate biotransformation and the concept of ontogeny, with further discussion of how to implement a developmental pharmacogenomics approach in future studies.
Glutathione transferase enzymes (GSTs) catalyze reactions in which electrophiles are conjugated to the tripeptide thiol glutathione. While many GST-catalyzed transformations result in the detoxication of xenobiotics, a few substrates, such as dihaloalkanes, undergo bioactivation to reactive intermediates. Many molecular epidemiological studies have tested associations between polymorphisms (especially, deletions) of human GST genes and disease susceptibility or response to therapy. This review presents a discussion of the biochemistry of GSTs, the sources—both genetic and environmental—of interindividual variation in GST activities, and their implications for pharmaco- and toxicogenetics; particular attention is paid to the Theta class GSTs.
Serum lactate dehydrogenase (LDH) is used in diagnosing many diseases and is significantly determined by genetic factors.Three genes coding for LDH isoenzymes were mapped to chromosome 11q15 and 12p12.We used 330 Framingham Heart Study largest families for microsatellite linkage scan and 100K SNPs association scan to determine quantitative trait loci of LDH level.We estimated the heritability at 41%.Our genome-wide linkage analysis yielded several chromosomal regions, other than 11q and 12p, with LOD scores between 1 and 2.5.None of the 100K SNPs with a P-value < 10 -4 in our genome-wide association study was close to the chromosomal regions where the LDH genes reside.Our study demonstrated a strong genetic effect on the variation of LDH levels.There may not be a single gene with a large effect, instead may be several genes with small effects in controlling the variation of serum LDH.Those genes may be located on chromosomal regions that differ from where the genes encoding LDH isoenzymes reside.
Warfarin, acenocoumarol, and phenprocoumon are among the major anticoagulant drugs worldwide. Because of their low therapeutic index and serious adverse reactions (ADRs), their wide use, and their varying kinetics and pharmacogenetic dependence, it is of great importance to explore further possibilities to forecast the dose beyond conventional INR measurements. Here, we describe particulars of the relative pharmacogenetic influence on the kinetics of these agents, the population distribution of genetics risk groups, and novel data on clinical features with influence on dose requirement and ADR risk. The usefulness of genetic information prior to and soon after start of therapy is also discussed. The current renewed focus on these issues is caused not only because of new genetic knowledge and genotyping facilities but also because of the high rate of serious ADRs. Application of these measures in the care of patients with anticoagulant therapy is important awaiting new therapeutic principles to be introduced, which may take long time still.
To identify potential pharmacodynamic biomarkers to guide dose selection in clinical trials using anti-interferon-alpha (IFN-α) monoclonal antibody (mAb) therapy for systemic lupus erythematosus (SLE), we used an Affymetrix human genome array platform and identified 110 IFN-α/β-inducible transcripts significantly upregulated in whole blood (WB) of 41 SLE patients. The overexpression of these genes was confirmed prospectively in 54 additional SLE patients and allowed for the categorization of the SLE patients into groups of high, moderate, and weak overexpressers of IFN-α/β-inducible genes. This approach could potentially allow for an accurate assessment of drug target neutralization in early trials of anti-IFN-α mAb therapy for SLE. Furthermore, ex vivo stimulation of healthy donor peripheral blood mononuclear cells with SLE patient serum and subsequent neutralization with anti-IFN-α mAb or anti-IFN-α receptor mAb showed that anti-IFN-α mAb has comparable effects of neutralizing the overexpression of type I IFN-inducible genes as that of anti-IFNAR mAb. These results suggest that IFN-α, and not other members of type I IFN family in SLE patients, is mainly responsible for the induction of type I IFN-inducible genes in WB of SLE patients. Taken together, these data strengthen the view of IFN-α as a therapeutic target for SLE.
In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified "predictor genes" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 "predictor genes" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.
Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed.
Gene expression profiling (GEP) of 8 stage 0/I untreated Chronic Lymphocytic Leukemia (CLL) patients showed over-expression of Frizzled 3 (FZD3)/ROR-1 receptor tyrosine kinase (RTK), FLT-3 RTK and CXCR3 G-protein coupled receptor (GPCR). RT-PCR of 24 genes in 21 patients of the WNT pathway corroborated the GEP. Transforming growth factorβ, fibromodulin, TGFβRIII and SMAD2 are also over-expressed by GEP. Serum cytokine profiling of 26 low stage patients showed elevation of IFNγ, CSF3, Flt-3L and insulin-like growth factor binding protein 4. In order to ascertain why CLL cells grow poorly in culture, a GEP of 4 CLL patients cells at 0 hr and 24 hr in culture demonstrated over expression of CXCL5, CCL2 and CXCL3, that may recruit immune cells for survival. Treatment with thalidomide, an immunomodulatory agent, showed elevation of CCL5 by GEP but was not cytotoxic to CLL cells. Our data suggest an interplay of several oncogenic pathways, cytokines and immune cells that promote a survival program in CLL.
Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats, and structures have become available. Among them are gene expression, single nucleotide polymorphism, copy number variation, and protein-protein/gene-gene interactions. Each of these distinct data types provides a different, partly independent and complementary, view of the whole genome. However, understanding functions of genes, proteins, and other aspects of the genome requires more information than provided by each of the datasets. Integrating data from different sources is, therefore, an important part of current research in genomics and proteomics. Data integration also plays important roles in combining clinical, environmental, and demographic data with high-throughput genomic data. Nevertheless, the concept of data integration is not well defined in the literature and it may mean different things to different researchers. In this paper, we first propose a conceptual framework for integrating genetic, genomic, and proteomic data. The framework captures fundamental aspects of data integration and is developed taking the key steps in genetic, genomic, and proteomic data fusion. Secondly, we provide a review of some of the most commonly used current methods and approaches for combining genomic data with focus on the statistical aspects.
Human Genomics and Proteomics (HGP) is a new genomics and systems biology journal that is affiliated with an international, open access database. In addition to publishing original research articles and review articles, the journal will also include short descriptions of genetic datasets pertaining to population/ethnic-specific mutation frequencies. HGP is the first scientific journal from SAGE-Hindawi Access to Research, a partnership designed to create a family of open access journals between the publishers SAGE and Hindawi.
